In silico prediction of physical protein interactions and characterization of interactome orphans

Nat Methods. 2015 Jan;12(1):79-84. doi: 10.1038/nmeth.3178. Epub 2014 Nov 17.


Protein-protein interactions (PPIs) are useful for understanding signaling cascades, predicting protein function, associating proteins with disease and fathoming drug mechanism of action. Currently, only ∼ 10% of human PPIs may be known, and about one-third of human proteins have no known interactions. We introduce FpClass, a data mining-based method for proteome-wide PPI prediction. At an estimated false discovery rate of 60%, we predicted 250,498 PPIs among 10,531 human proteins; 10,647 PPIs involved 1,089 proteins without known interactions. We experimentally tested 233 high- and medium-confidence predictions and validated 137 interactions, including seven novel putative interactors of the tumor suppressor p53. Compared to previous PPI prediction methods, FpClass achieved better agreement with experimentally detected PPIs. We provide an online database of annotated PPI predictions ( and the prediction software (

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Validation Study

MeSH terms

  • Computational Biology / methods*
  • Computer Simulation*
  • Data Mining / methods*
  • Humans
  • Protein Interaction Mapping / methods*
  • Proteome
  • Software
  • Tumor Suppressor Protein p53 / physiology


  • Proteome
  • Tumor Suppressor Protein p53